Methods, systems, and devices for detecting sleep and apnea events

ABSTRACT

Described herein are methods, devices, and systems that use electrogram (EGM) or electrocardiogram (ECG) data for sleep apnea detection. An apparatus and method detect potential apnea events (an apnea or hypopnea event) using a signal indicative of cardiac electrical activity of a patient&#39;s heart, such as an EGM or ECG. Variations in one or more morphological or temporal features of the signal over several cardiac cycles are determined and used to detect a potential apnea event in a measurement period. Checks can then be made for a number of factors which could result in a false detection of an apnea event and if such factors are not present, an apnea event is recorded. Described herein are also methods, devices, and systems for classifying a patient as being asleep or awake, which can be used to selectively enable and disable sleep apnea detection monitoring, as well as in other manners.

PRIORITY CLAIM

The present application claims priority to each of the followingapplications: U.S. Provisional Patent Application No. 63/088,947, titledMETHODS, SYSTEMS, AND DEVICES FOR DETECTING SLEEP AND APNEA EVENTS,which was filed on Oct. 7, 2020; U.S. Provisional Patent Application No.63/052,877, titled DETECTING APNEA EVENTS USING A CARDIAC SIGNAL, whichwas filed on Jul. 16, 2020; and U.S. Provisional Patent Application No.63/033,553, titled DETECTING APNEA EVENTS USING A CARDIAC SIGNAL, whichwas filed on Jun. 2, 2020. Each of the above applications isincorporated by reference herein.

FIELD OF TECHNOLOGY

Embodiments described herein generally relate to methods, systems, anddevices (aka apparatuses) for detecting apnea events in human patients,and more particularly, for detecting apnea events using cardiac rhythms.Embodiments described also relate to methods, systems, and devices fordetecting when a patient is awake, and when a patient is asleep, whichinformation can be used to selectively enable and disable monitoring forsleep apnea events, and/or in other manners.

BACKGROUND

Sleep apnea syndrome (SAS) is a breathing disorder characterized byrepeated episodes of reduced (hypopnea) or absent (apnea) airflow. SASis common in middle-aged women and men and can lead myriad maladiesadversely affecting one's quality of life. It can be present with othercardiovascular comorbidities such as arrhythmia, coronary arterydisease, and heart failure. One method of diagnosing SAS is sleep studyvia overnight polysomnography (PSG). However, this method is costly,uncomfortable for the patient and requires extended evaluation time.

Various types of devices can provide cardiogram data normally used fordiagnosing cardiac issues. Such devices include surface (skin) mountedmedical devices and implantable medical devices (IMDs). Some types ofIMDs, such as implantable cardiac pacemakers and implantable cardiacdefibrillators (ICDs), are capable of providing not only data, buttreatment of cardiac issues. Other types of IMDs, such as insertablecardiac monitors (ICMs), are used for diagnostic purposes.

Techniques have been developed using implantable devices to useimpedance-based techniques to detect SAS and provide long-terminformation on changes in the severity of SAS over time. Impedance baseddetectors require a dedicated impedance module for SAS assessment, whichmay not be readily available in all IMDs. Intracardiac electrogram(IEGM) is readily available in almost all ICDs. Computationally basedIMD SAS monitoring was developed using a frequency domain-based, FastFourier Transfer algorithm. However, this algorithm is computationallyintensive, which can require significant current drain from the batterypowering an IMD device.

SAS only occurs while a person is asleep. Accordingly, when monitoringfor SAS, it is helpful to know whether a person is asleep or awake.

SUMMARY

Certain embodiments of the present technology are directed toapparatuses, methods, and non-transitory computer readable mediumstoring instructions that can be used to detect an apnea event. Such anapparatus can comprise sensing circuitry couplable to electrodes andconfigured to sense a signal indicative of cardiac electrical activityof a patient's heart. The apparatus can also include at least oneprocessor configured to determine a measure of short-term variation (SW)and a measure of long-term variation (LW) in a feature of the signalover a measurement period that includes a plurality of cardiac cycles,and detect a potential apnea event in the measurement period based onthe measure of SW and the measure of LTV in the feature; wherein thefeature comprises one of a morphological feature or a temporal feature.In accordance with certain embodiments, the apparatus comprises one ofan implantable pacemaker, an implantable cardiac defibrillator, animplantable cardiac monitor, or a non-implanted apparatus.

In accordance with certain embodiments, the at least one processor ofthe apparatus is configured to detect the potential apnea event bydetermining that the measure of LW is greater than the measure of STVfor at least the measurement period. In accordance with otherembodiments, the at least one processor of the apparatus is configuredto detect the potential apnea event by determining that the measure ofLW is greater than the measure of SW for at least the measurementperiod, and the measure of STV is less that a specified threshold for atleast the measurement period.

In accordance with certain embodiments, the at least one processor ofthe apparatus is configured to: identify individual cardiac cycleswithin the signal indicative of cardiac electrical activity of apatient's heart; determine the measure of STV based on a first number ofthe individual cardiac cycles; and determine the measure of LW based ona second number of the individual cardiac cycles, wherein the secondnumber is greater than the first number.

In accordance with certain embodiments, the feature of the signal, forwhich the measure of STV and the measure of LTV are determined over themeasurement period, comprises a morphological feature, which comprisesone of: an area under a curve of a QRS complex, R-wave, T-wave,ST-region, or evoked response; a maximum amplitude of a QRS complex,R-wave, T-wave, or evoked response; or a peak-to-peak amplitude of a QRScomplex, R-wave, T-wave, or evoked response.

In accordance with certain embodiments, the feature of the signal, forwhich the measure of STV and the measure of LTV are determined over themeasurement period, comprises a temporal feature, which comprises oneof: RR interval duration; PR interval duration; QT interval duration;QRS complex duration; AR interval duration; PV interval duration; orevoked response duration.

In accordance with certain embodiments, each of the measure of STV andthe measure of LTV in the feature of the signal over the measurementperiod is one of: a standard deviation of the feature; a standarddeviation of the feature divided by an average of the feature; adifference between a maximum and a mean of the feature; a differencebetween a maximum and a minimum of the feature; a difference between asecond maximum and a second mean of the feature; a difference betweenthen Nth maximum and Nth mean of the feature; or any one of saiddifferences divided by an average of the feature.

In accordance with certain embodiments, the apparatus further comprisesan accelerometer that alone or in combination with the at least oneprocessor is used to obtain posture information and activityinformation, wherein the at least one processor is further configuredto: classify the patient as being asleep based on the postureinformation and the activity information; classify the patient as beingawake based on at least one of the posture information or the activityinformation; enable the determining of the measure of STV and themeasure of LTV that are used to detect the potential apnea event, inresponse to the patient being classified as being asleep; and disablethe determining of the measure of SW and the measure of LTV that areused to detect the potential apnea event, in response to the patientbeing classified as being awake.

In accordance with certain embodiments, the at least one processor isfurther configured to determine whether the potential apnea event is atrue apnea event or a false detection by analyzing a portion of thesignal preceding the measurement period in which the potential apneaevent was detected to determine whether a heart rhythm change likelycaused the potential apnea event to be detected.

A method for monitoring for apnea, according to certain embodiments ofthe present technology, comprises: obtaining a signal indicative ofcardiac electrical activity of a patient's heart; determining a measureof short-term variation (SW) and a measure of long-term variation (LTV)of a feature of the signal over a measurement period that includes aplurality of cardiac cycles; and detecting a potential apnea event inthe measurement period based on the measure of STV and the measure of LWin the feature; wherein the feature comprises one of a morphologicalfeature or a temporal feature.

In accordance with certain embodiments, the method further comprisesidentifying individual cardiac cycles within the signal; and wherein themeasure of SW is determined based on a first number of the individualcardiac cycles, and the measure of LW is determined based on a secondnumber of the individual cardiac cycles, wherein the second number isgreater than the first number.

In accordance with certain embodiments, the method further comprisesdetermining whether the potential apnea event that is detected is a trueapnea event or a false detection by analyzing a portion of the signalpreceding the measurement period in which the potential apnea event wasdetected to determine whether a heart rhythm change, a presence of anon-cardiac signal, or a change in the feature is due to a change inpatient posture or patient activity likely caused the potential apneaevent to be detected; and in response to determining that the potentialapnea event is a true apnea event, storing or uploading informationabout the true apnea event so that the information can be accessed by amedical practitioner; wherein the heart rhythm change comprises at leastone of: multiple cardiac rhythms comprising a pacing rhythm inconjunction with an intrinsic rhythm; one or more premature ventricularcontractions; or one or more premature atrial contractions.

In accordance with certain embodiments, the detecting the potentialapnea event occurs in response to the measure of LW of the feature beinggreater than the measure of SW of the feature for at least themeasurement period. In accordance with other embodiments, the detectingthe potential apnea event occurs in response to both the measure of LWof the feature being greater than the measure of SW for at least themeasurement period, and the measure of STV of the feature being lessthat a specified threshold for at least the measurement period.

In accordance with certain embodiments, the feature of the signal, forwhich the measure of STV and the measure of LTV are determined over themeasurement period, comprises a morphological feature, which comprisesone of: an area under a curve of a QRS complex, R-wave, T-wave,ST-region, or evoked response; a maximum amplitude of a QRS complex,R-wave, T-wave, or evoked response; or a peak-to-peak amplitude of a QRScomplex, R-wave, T-wave, or evoked response.

In accordance with certain embodiments, the feature of the signal, forwhich the measure of STV and the measure of LTV are determined over themeasurement period, comprises a temporal feature, which comprises oneof: RR interval duration; PR interval duration; QT interval duration;QRS complex duration; AR interval duration; PV interval duration; orevoked response duration.

In accordance with certain embodiments, each of the measure of STV andthe measure of LTV in the feature of the signal over the measurementperiod is one of: a standard deviation of the feature; a standarddeviation of the feature divided by an average of the feature; adifference between a maximum and a mean of the feature; a differencebetween a maximum and a minimum of the feature; a difference between asecond maximum and a second mean of the feature; a difference betweenthen Nth maximum and Nth mean of the feature; or any one of saiddifferences divided by an average of the feature.

In accordance with certain embodiments, the method further comprisesusing an accelerometer to obtain posture information and activityinformation; classifying the patient as being asleep based on theposture information and the activity information; in response toclassifying the patient as being asleep, enabling the determining of themeasure of STV and the measure of LTV that are used for detecting thepotential apnea event; classifying the patient as being awake based onat least one of the posture information or the activity information; andin response to classifying the patient as being awake, disabling thedetermining of the measure of SW and the measure of LTV that are usedfor detecting the potential apnea event.

In accordance with certain embodiments of the present technology, anon-transitory computer readable medium stores instructions to detect anapnea event that, when executed by a processor, causes the processor toperform operations, comprising: obtaining a signal indicative of cardiacelectrical activity of a patient's heart; determining a measure ofshort-term variation (STV) and a measure of long-term variation (LW) ina feature of the signal over a measurement period that includes aplurality of cardiac cycles; and detecting a potential apnea event inthe measurement period based on the measure of SW and the measure of LTVin the feature; wherein the feature comprises one of a morphologicalfeature or a temporal feature. The non-transitory computer readablemedium can also include instructions that further cause the processor toperform other steps and features of the above summarized methods.

Certain embodiments of the present technology are directed toapparatuses, methods, and non-transitory computer readable mediumstoring instructions that can be used to classify a patient as eitherbeing asleep or awake. Such an apparatus can include an accelerometerand at least one processor. The accelerometer, alone or in combinationwith the at least one processor, is used to determine an activity levelof a patient and a posture of the patient. The at least one processor isconfigured to: classify the patient as being asleep in response to both(i) the posture of the patient being recumbent or reclined for at leasta sleep latency duration, and (ii) the activity level of the patient notexceeding an activity threshold for at least the sleep latency duration;and classify the patient as being awake in response to at least one of(iii) the posture of the patient being upright for at least an awakelatency duration, or (iv) the activity level of the patient exceedingthe activity threshold for at least the awake latency duration. Inaccordance with certain embodiments, the apparatus comprises animplantable medical device, such as an implantable pacemaker, animplantable cardiac defibrillator, or an implantable cardiac monitor.

In accordance with certain embodiments, the at least one processor ofthe apparatus is further configured to: detect the activity level of thepatient over a period of time; produce a histogram based on the activitylevel of the patient over the period of time, the histogram including aplurality of bins each of which is associated with a different activitylevel and includes a respective number of activity counts; and determinethe activity threshold based on the histogram.

In accordance with certain embodiments, the at least one processor ofthe apparatus is configured to: determine a total number of activitycounts included in the histogram; determine the activity levelcorresponding to a specified percent of the total number of activitycounts; and determine the activity threshold as being equal to theactivity level corresponding to the specified percent of the totalnumber of activity counts.

In accordance with certain embodiments, the at least one processor ofthe apparatus is configured to: determine one of a long term average(LTA) or a long term moving average (LTMA) of the activity level of thepatient over a period of time; and determine the activity threshold asbeing equal to the one of the LTA or the LTMA of the activity level ofthe patient, or as being equal to the one of the LTA or the LTMA of theactivity level of the patient plus a specified offset.

In accordance with certain embodiments, the one of the LTA or the LTMAchanges over time, and thus, the activity threshold determined by the atleast one processor also changes over time.

In accordance with certain embodiments, the at least one processor ofthe apparatus is further configured to: enable monitoring for apnea inresponse to the patient being classified as being asleep; and disablemonitoring for apnea in response to the patient being classified asbeing awake. In accordance with certain embodiments, the at least oneprocessor is further configured to: enable monitoring of sleep qualityin response to the patient being classified as being asleep; and disablemonitoring of sleep quality in response to the patient being classifiedas being awake.

Certain embodiments of the present technology are directed to a methodcomprising: determining an activity level of a patient; determining aposture of the patient; during a first period of time, classifying thepatient as being asleep in response to both (i) the posture of thepatient being recumbent or reclined for at least a sleep latencyduration, and (ii) the activity level of the patient not exceeding anactivity threshold for at least the sleep latency duration; and during asecond period of time, classifying the patient as being awake inresponse to at least one of (iii) the posture of the patient beingupright for at least an awake latency duration, or (iv) the activitylevel of the patient exceeding the activity threshold for at least theawake latency duration.

In accordance with certain embodiments, the determining the activitylevel of the patient and the determining the posture of the patient areperformed using an accelerometer. In accordance with certainembodiments, the method is performed by an implantable medical devicethat includes the accelerometer.

In accordance with certain embodiments, the method further comprises:detecting the activity level of the patient over a period of time;producing a histogram based on the activity level of the patient overthe period of time, the histogram including a plurality of bins each ofwhich is associated with a different activity level and includes arespective number of activity counts; and determining the activitythreshold based on the histogram.

In accordance with certain embodiments, the determining the activitythreshold based on the histogram comprises: determining a total numberof activity counts included in the histogram; determining the activitylevel corresponding to a specified percent of the total number ofactivity counts; and determining the activity threshold as being equalto the activity level corresponding to the specified percent of thetotal number of activity counts.

In accordance with certain embodiments, the method further comprises:determining one of a long term average (LTA) or a long term movingaverage (LTMA) of the activity level of the patient over a period oftime; and determining the activity threshold as being equal to the oneof the LTA or the LTMA of the activity level of the patient, or as beingequal to the one of the LTA or the LTMA of the activity level of thepatient plus a specified offset. In accordance with certain embodiments,the one of the LTA or the LTMA changes over time, and thus, the activitythreshold also changes over time.

In accordance with certain embodiments, the method further comprises:enabling monitoring for apnea in response to the patient beingclassified as being asleep; and disabling monitoring for apnea inresponse to the patient being classified as being awake. Alternatively,or additionally, the method further comprises: enabling monitoring ofsleep quality in response to the patient being classified as beingasleep; and disabling monitoring of sleep quality in response to thepatient being classified as being awake.

In accordance with certain embodiments of the present technology, anon-transitory computer readable medium stores instructions that whenexecuted by a processor cause the processor to perform operations,comprising: determining an activity level of a patient; determining aposture of the patient; classifying the patient as being asleep inresponse to both (i) the posture of the patient being recumbent orreclined for at least a sleep latency duration, and (ii) the activitylevel of the patient not exceeding an activity threshold for at leastthe sleep latency duration; and classifying the patient as being awakein response to at least one of (iii) the posture of the patient beingupright for at least an awake latency duration, or (iv) the activitylevel of the patient exceeding the activity threshold for at least theawake latency duration. The non-transitory computer readable medium canalso include instructions that further cause the processor to performother steps and features of the above summarized methods.

This summary is not intended to be a complete description of theembodiments of the present technology. Other features and advantages ofthe embodiments of the present technology will appear from the followingdescription in which the preferred embodiments have been set forth indetail, in conjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present technology relating to both structure andmethod of operation may best be understood by referring to the followingdescription and accompanying drawings, in which similar referencecharacters denote similar elements throughout the several views:

FIG. 1 is a block diagram of an implantable medical device suitable forimplementing the present technology.

FIG. 2 illustrates an EGM segment, a plot of the R-wave amplitudevariability of the signal in the segment, a plot of the short-termvariability (SW) of peak to peak (P2P) R-wave amplitude of the signal inthe segment, and a plot of the long-term variability (LW) of P2P R-waveamplitude of the signal in the segment.

FIG. 3 illustrates a general method for detecting apnea events, inaccordance with an embodiment of the present technology.

FIG. 4A illustrates one method for performing steps 310 and 320 of FIG.3, in accordance with an embodiment of the present technology.

FIG. 4B illustrates another method for performing steps 310 and 320 ofFIG. 3, in accordance with an embodiment of the present technology.

FIG. 4C illustrates yet another method for performing steps 310 and 320of FIG. 3, in accordance with an embodiment of the present technology.

FIG. 5 illustrates one method for performing step 330 of FIG. 3, inaccordance with an embodiment of the present technology.

FIG. 6 shows an example of proper detection of one apnea/hypopnea eventby the present technology.

FIG. 7A illustrates a method for detecting sleep entry, in accordancewith an embodiment of the present technology.

FIG. 7B shows example pseudocode that can be used to implement themethod summarized with reference to the flow diagram of FIG. 7A.

FIG. 8A illustrates a method for detecting sleep exit, in accordancewith an embodiment of the present technology.

FIG. 8B shows example pseudocode that can be used to implement themethod summarized with reference to the flow diagram of FIG. 8A.

FIG. 9 illustrates how an embodiment that is used to monitor for apotential sleep apnea event can be enabled in response to sleep entrybeing detected, and can be disabled in response to sleep exit beingdetected, in accordance with specific embodiments of the presenttechnology.

FIG. 10 illustrates how the sleep entry and sleep exit techniques,described with reference to FIGS. 6 and 7, can be used to monitor theamount of sleep a patient gets continuously and/or within a specifiedperiod of time.

FIG. 11A is an example graph showing activity data collected over anumber of days.

FIG. 11B illustrates an example of a histogram that can be used todetermine an activity threshold, for use in the sleep entry and sleepexit detection techniques, in accordance with certain embodiments of thepresent technology, wherein the histogram is produced using the activitydata shown in FIG. 11A.

FIG. 12 illustrates exemplary results of using an embodiment of thepresent technology to classify when a patient is awake and when thepatient is asleep.

DETAILED DESCRIPTION

Technology is presented for electrogram (EGM) or electrocardiogram (ECG)based SAS detection. The technology provides a monitoring algorithm thatis time-domain based in its computational approach and does not requireextra circuitry for implementation in a surface or implanted device. TheEGM or ECG features are easily extracted, and the computationalrequirement has minimal impact on the battery longevity when used inIMDs or surface mounted devices. In one aspect, the technology is anapparatus and method which detects potential apnea events (an apnea orhypopnea event) using a signal indicative of cardiac electrical activityof a patient's heart, such as in EGM or ECG signal. The technology usesvariations in one or more morphological or temporal features of thesignal over a number of cardiac cycles to detect a potential apnea eventin a measurement period. The technology then checks for a number offactors which could result in a false detection of an apnea event and ifsuch factors are not present, an apnea event is recorded. The technologymay be included in an IMD or surface mounted device which may includeaspects of the technology to perform the) detection and reportingmethods described herein in real-time as a patient sleeps, or mayprovide data to another device which then analyzes the data afterrecording.

FIG. 1 is a block diagram of an example IMD 102, which can be a leadlesscardiac pacemaker (LCP), traditional pacemaker, implantable cardiacdefibrillator (ICD), neurostimulator, insertable cardiac monitor, or thelike. The IMD 102 is shown as including a microcontroller unit (MCU)104, pulse generator(s) 106, sensing amplifier(s) 108, physiologicsensor(s) 110, electrode switches 112, an RF telemetry module 114, apower supply 116, an accelerometer 125 and memory 120.

The MCU 104 can control various modes of stimulation therapy. As is wellknown in the art, the MCU 104 (also referred to herein as a control unitor controller) typically includes a microprocessor, or equivalentcontrol circuitry, designed specifically for controlling the delivery ofstimulation therapy and may further include RAM or ROM memory, logic andtiming circuitry, state machine circuitry, and I/O circuitry. Typically,the MCU 104 includes the ability to process or monitor input signals(data) as controlled by a program code stored in a designated block ofmemory. The details of the design and operation of the MCU 104 are notcritical to the technology. Rather, any suitable MCU 104 that includesat least one processor may be used that carries out the functionsdescribed herein. The use of microprocessor-based control circuits forperforming timing and data analysis functions are well known in the art.In certain embodiments, the MCU 104 is used to implement a sleep timer134 and an awake timer 136, as shown in FIG. 1. Such timers 134, 136 canalternatively be implemented using circuitry that is external to the MCU104, but in communication with the MCU 104. The use of such timers 134,136 are discussed below with reference to FIGS. 7A-9.

Where the IMD 102 is a cardiac stimulation device, the pulsegenerator(s) 106 can include an atrial pulse generator and a ventricularpulse generator that generate pacing stimulation pulses for delivery tocardiac tissue via electrodes. Such electrodes can be included on leads,or can be on or adjacent a housing 103 of the IMD 102, e.g., if the IMD102 is an LCP. Where more than two electrodes are available fordelivering stimulation pulses, the electrode switches 112 can be used toselect specific combinations of electrodes under the control of the MCU104. It is understood that in order to provide stimulation therapy inone or more of the four chambers of the heart, atrial and/or ventricularpulse generators (e.g., 106) may include dedicated, independent pulsegenerators, multiplexed pulse generators or shared pulse generators. Thepulse generator(s) 106 are controlled by the MCU 104 via appropriatecontrol signals to trigger or inhibit the stimulation pulses. Where theIMD 102 is a neurostimulator, the pulse generator(s) 106 can producestimulation pulses that are for use in performing spinal cordstimulation (SCL), dorsal root ganglion (DRG) stimulation, deep brainstimulation (DBS), and/or the like. In the below description, unlessstated otherwise, it will be assumed that the IMD 102 is a cardiacstimulation device.

Where the IMD 102 is a cardiac stimulation device, the MCU 104 caninclude a timing control module to control the timing of the stimulationpulses, including, but not limited to, pacing rate, atrioventricular(AV) delay, interatrial conduction (AA) delay, interventricularconduction (VV) delay and/or intraventricular delay (e.g., LV1-LV2delay). The timing control module can also keep track of the timing ofrefractory periods, blanking intervals, noise detection windows, evokedresponse detection windows, alert intervals, marker channel timing,etc., which is well known in the art. The MCU 104 can also include anapnea detector that can be used in accordance with the methods discussedherein. The MCU 104 can also include a capture detection module and/or amorphology detection module. Depending upon the implementation, thevarious components of the MCU 104 may be implemented as separatesoftware modules or the modules may be combined to permit a singlemodule to perform multiple functions. In addition, although described asbeing components of the MCU 104, some or all of the above discussedmodules may be implemented separately from the MCU 104, e.g., using oneor more application specific integrated circuits (ASICs) or the like.

The electrode switches 112, which can also be referred to as switchingcircuitry 112, includes a plurality of switches for connecting thedesired electrodes to the appropriate I/O circuits, thereby providingcomplete electrode programmability. Accordingly, the switching circuitry112, in response to a control signal from the MCU 104, determines thepolarity of the stimulation pulses (e.g., unipolar, bipolar, combipolar,etc.) by selectively closing the appropriate combination of switches(not shown) as is known in the art. The switching circuitry 112 can alsoswitch among the various different combinations of electrodes.

The sensing amplifier(s) 108 can include, e.g., atrial and/orventricular sensing amplifiers that are selectively coupled to variouscombinations of electrodes to provide for various different sensingvectors that can be used, e.g., for detecting the presence of cardiacactivity in one or more of the four chambers of the heart. Accordingly,the sensing amplifier(s) 108 can include dedicated sense amplifiers,multiplexed amplifiers or shared amplifiers. The switching circuitry 112determines the “sensing polarity” of the cardiac signal by selectivelyclosing the appropriate switches, as is also known in the art. In thisway, a clinician may program the sensing polarity independent of thestimulation polarity. Each sensing amplifier 108 can employ one or morelow power, precision amplifiers with programmable gain and/or automaticgain control, bandpass filtering, and a threshold detection circuit, asknown in the art, to selectively sense the cardiac signal of interest.The automatic gain control enables the IMD 102 to deal effectively withthe difficult problem of sensing the low amplitude signalcharacteristics of atrial or ventricular fibrillation. The outputs ofthe sensing amplifier(s) 108 are connected to the MCU 104 which, inturn, is able to trigger or inhibit the one or more pulse generator(s)106 in a demand fashion in response to the absence or presence ofcardiac activity in the appropriate chambers of the heart.

For apnea detection, the MCU 104 utilizes the sensing amplifier(s) 108to sense cardiac signals to acquire a cardiac signal. As used in thissection “sensing” is reserved for the noting of an electrical signal,and “detection” is the processing of these sensed signals and noting thepresence of an apnea event or some other event being monitored for.

Although not specifically shown in FIG. 1, cardiac signals can alsoapplied to the inputs of an analog-to-digital (A/D) data acquisitionsystem that is configured to acquire intracardiac electrogram signals,convert the raw analog data into a digital signal, and store the digitalsignals for later processing and/or telemetric transmission to anexternal programmer or a bedside monitor or personal advisory module(PAM). The data acquisition system can be coupled to various leadsand/or electrodes through the switching circuitry 112 to sample cardiacsignals across any pair of desired electrodes. The MCU 104 is furthercoupled to the memory 120 by a suitable data/address bus, or the like,wherein the programmable operating parameters used by the MCU 104 arestored and modified, as required, in order to customize the operation ofIMD 102 to suit the needs of a particular patient. Such operatingparameters define, for example, the amplitude or magnitude, pulseduration, electrode polarity, for both pacing pulses and impedancedetection pulses as well as pacing rate, sensitivity, apnea detectioncriteria, and the amplitude, waveshape and vector of each pacing andshocking pulse to be delivered to the patient's heart within eachrespective tier of therapy. Other pacing parameters include base rate,rest rate and circadian base rate.

Advantageously, the operating parameters of the IMD 102 may benon-invasively programmed into the memory 120 through an RF telemetrycircuit 114 in telemetric communication with an external device orbedside monitor 132, such as a programmer, transtelephonic transceiveror a diagnostic system analyzer. The RF telemetry circuit 114, which canalso be referred to as an RF communication subsystem, is activated bythe MCU 104 by a control signal. The RF telemetry circuit 114advantageously allows intracardiac electrograms and status informationrelating to the operation of the IMD 102 (as contained in the MCU 104 ormemory 120) to be sent to the external device 132 through an establishedcommunication link. An internal warning device, not specifically shown,may be provided for generating perceptible warning signals to thepatient via vibration, voltage or other methods.

The memory 120 may include instructions operable to cause the MCU 104 toperform the methods described herein. In one embodiment, memory 120 maycomprise a non-volatile, non-transitory computer readable medium and/orvolatile memory containing such instructions. Alternatively, the MCU 104may include an internal computer readable medium or memory including theinstructions.

The physiologic sensors 110 can include a temperature sensor 111, anaccelerometer 113, and/or other types of physiologic sensors, commonlyreferred to as a “rate-responsive” sensor because they can be used toadjust pacing stimulation rate according to the exercise state of thepatient. However, the physiological sensor(s) 110 may further be used todetect changes in cardiac output, changes in the physiological conditionof the heart, or diurnal changes in activity (e.g., detecting sleep andwake states) and to detect arousal from sleep. While shown as beingincluded within the IMD 102, it is to be understood that one or more ofthe physiologic sensor(s) 110 may also be external to the IMD 102, yetstill be implanted within or carried by the patient. A common type ofrate responsive sensor is an activity sensor incorporating anaccelerometer or a piezoelectric crystal, which is mounted within thehousing 103 of the IMD 102. Other types of physiologic sensors are alsoknown, for example, sensors that sense the oxygen content of blood,respiration rate and/or minute ventilation, pH of blood, ventriculargradient, stroke volume, cardiac output, contractility, etc.

The power supply 116, which can include a battery 117 and a voltageregulator 118, provides operating power to all of the circuits orsubsystem shown in FIG. 1. The specific type of battery 117 included inthe IMD 102 can vary depending on the capabilities of IMD 102. If theIMD 102 only provides low voltage therapy, a lithium iodine or lithiumcopper fluoride cell typically may be utilized as the battery 117. Ifthe IMD 102 provides shocking therapy, the battery 117 should be capableof operating at low current drains for long periods, and then be capableof providing high-current pulses (for capacitor charging) when thepatient requires a shock pulse. The battery 117 should also have apredictable discharge characteristic so that elective replacement timecan be detected. Accordingly, appropriate batteries are employed. One ormore voltage regulators 118 can step up or step down a voltage provideby the battery 117 to produce one or more predetermined voltages usefulfor powering the various circuits or subsystems of the IMD 102.

The IMD 102 can include additional and/or alternative types of circuitsor subsystems, not specifically shown in FIG. 1. For example, the IMD102 can also include an impedance measurement circuit that can be usedfor providing lead impedance surveillance during the acute and chronicphases for proper lead positioning or dislodgement; detecting operableelectrodes and automatically switching to an operable pair ifdislodgement occurs; measuring respiration or minute ventilation;measuring thoracic impedance for determining shock thresholds; detectingwhen the device has been implanted; measuring respiration; and detectingthe opening of heart valves, etc. Such an impedance measurement circuitcan be coupled to the switching circuitry 112 so that any desiredcombination of electrodes may be used.

The above described IMD 102 was described as an exemplary cardiacstimulation or detection device. One of ordinary skill in the art wouldunderstand that embodiments of the present technology can be used withalternative types of implantable devices. Accordingly, embodiments ofthe present technology should not be limited to use only with the abovedescribed device.

The RF telemetry circuit 114 can be the Bluetooth Low Energy (BLE)radio, or some other RF communication subsystem, that is implemented inan RF integrated circuit (IC). The remaining set of circuits orsubsystems of the IMD 102 shown in FIG. 1, or just a subset thereof, canbe implemented in a custom application specific IC (ASIC), which canalso be referred to as a custom chip. In other words, the terms IC andchip are used interchangeably herein. Depending on the specific IMD,there may be additionally IC's. The custom IC can host the IMD'sapplication and have all the associated circuits for sensing, pacing,high voltage (HV) therapy, etc. The RF chip, which is used to provide RFcommunication, can include a high-speed (aka high frequency) crystaloscillator. The connection between the RF chip and the custom chip istypically a standard serial interface, such as serial peripheralinterface (SPI) and a few general-purpose input-outputs (GPIO), but canalternatively or additionally include a parallel interface.

It is well known that each cardiac cycle represented within an EGM or anelectrocardiogram (ECG) typically includes a P-wave, followed by a QRScomplex, followed by a T-wave, with the QRS complex including Q-, R-,and S-waves. The P-wave is caused by depolarization of the atria. Thisis followed by atrial contraction, which is indicated by a slight risein atrial pressure contributing to further filling of the ventricle.Following atrial contraction is ventricular depolarization, as indicatedby the QRS complex, with ventricular depolarization initiatingcontraction of the ventricles resulting in a rise in ventricularpressure until it exceeds the pulmonary and aortic diastolic pressuresto result in forward flow as the blood is ejected from the ventricles.Ventricular repolarization occurs thereafter, as indicated by the T-waveand this is associated with the onset of ventricular relaxation in whichforward flow stops, the pressure in the ventricle falls below that inthe atria at which time the mitral and tricuspid valves open to begin topassively fill the ventricle during diastole. The terms EGM, EGM signal,and EGM waveform are used interchangeably herein. Similarly, the termsECG, ECG signal, and ECG waveform are used interchangeably herein. BothECG and EGM signals are signals indicative of electrical activity of apatient's heart, which can also be referred to as cardiac electricalsignals, or the like.

The R-wave is the largest wave in the QRS complex, and it oftenidentified by comparing samples of an EGM or ECG to an R-wave threshold.Various measurements can be obtained based on the EGM or ECG waveform,including measurements of R-R intervals, where an R-R interval is theduration between a pair of consecutive R-waves.

As will be described in additional detail below, certain embodiments ofthe present technology described herein provide for sleep apneadiscrimination by distinguishing between irregular patterns in thevariability of the morphology of a QRS complex. Although the presenttechnology will be described with respect to use of variability of theR-wave, any of the morphological features of the QRS complex may be usedin accordance with the present technology. For example, where elementsof the R-wave are described herein, one may substitute use of the T-wavemorphology in the analysis.

FIG. 2 at the top illustrates an example EGM segment 220 ofapproximately eighty (80) seconds (between time markers 100 and 180seconds). FIG. 2 in the middle illustrates a plot 230 of the R-waveamplitude variability of the signal in the segment 220. FIG. 2 at thebottom illustrates a plot 250 of the short-term variability (STV) ofpeak-to-peak (P2P) R-wave amplitude of the signal in the EGM segment220, and a plot 240 of the long-term variability (LW) of P2P R-waveamplitude of the signal in the EGM segment 220. Certain embodiments ofthe present technology take advantage of the realization that R-waveamplitude variability changes during periods of reduced or absentairflow. Thus, the significant reduction of R-wave amplitude variabilityin window 210 between the 135 and 155 second time markers indicates a nobreathing phase (NBP), which can also be referred to as a non-breathingperiod (NBP). This reduction of R-wave amplitude variability in window210 is a discernable change from the R-wave amplitude variation duringnormal breathing (prior to window 210 and after window 210) and mayindicate a potential apnea event. The term short-term variability (SW)can also be referred to interchangeably herein as the short-termvariation (STV), or a measure of STV. Similarly, the term long-termvariability (LW) can also be referred to herein as the long-termvariability (LW), or a measure of LW.

Certain embodiments of the present technology use this characteristic ofthe signal to detect this NBP by comparing the SW to the LTV of thefeature of the signal. In particular, in certain embodiments acomparison of STV and LTV of a feature of the R-wave is used to detect anon-breathing period indicating a potential apnea event. In normalbreathing, the STV of the R-wave amplitude crosses LW periodically,whereas the STV of the R-wave amplitude stays below LTV during theentire duration of a non-breathing period. In addition, the SW is muchlower in amplitude during a non-breathing period as compared to a normalbreathing period. These two characteristics of the signal are used todetect potential apnea events.

Although the present technology is described in one aspect as using acomparison of STV and LTV of R-wave amplitude, other alternatives may beused to determine a potential apnea event. For example, one may use acoarser determination of determining a potential apnea event when STV ofthe R-wave amplitude (or T-wave amplitude or other morphologicalfeature) drops below a pre-defined threshold, without comparing the SWto the LTV.

FIG. 3 illustrates a general method for detecting apnea events, inaccordance with certain embodiments of the present technology. At step300, a signal indicative of cardiac electrical activity of a heart isobtained. Such a signal can be an electrocardiogram (ECG) or anelectrogram (EGM), depending upon whether the signal is obtained by anon-implanted device or an implanted device. For example, the signal canbe an EGM obtained using an IMD, such as the IMD 100 described abovewith reference to FIG. 1. The signal may be a measure of an intrinsicrhythm or a ventricular pacing rhythm. In alternative embodiments, anexternal (i.e., non-implanted) medical device such as a Holter monitormay be utilized to obtain an ECG. In certain embodiments, where IMD 100is utilized, the EGM signal is only obtained while a patient is sleeping(or at least likely to be sleeping) as determined by the MCU 104 basedon a signal received from the accelerometer 125 that indicates thepatient is at rest. In other embodiments, the EGM signal may also beobtained during one or more periods of time when the patient is notsleeping, but the EGM signal is only used to detect a potential apneaevent when it is determined that the patient is likely to be sleeping.More generally, in certain embodiments, a method for monitoring forapnea is only performed when it is determined that a patient issleeping, or at least likely to be sleeping. Various techniques fordetermining that a patient is sleeping (or at least likely to besleeping) may be utilized including, but not limited to, the time ofday, and/or the techniques described below with reference to FIG. 7A-12.

At step 310, there is a determination of at least one measure ofvariation in a feature of the signal (e.g., EGM or ECG) during ameasurement period that includes a plurality of cardiac cycles. Themeasurement period may vary, but in one embodiment the measurementperiod has a duration of about 10 seconds. (Ten seconds withoutbreathing is the period of time generally recognized as evincing an SASevent.) The use of shorter or longer measurement periods are alsopossible and within the scope of the embodiments described herein,although it is preferred that the measurement period be at least 10seconds. The feature(s) for which at least one measure of variation isdetermined can be a morphological feature and/or a temporal feature. Asnoted above, in some embodiments, the at least one measure of variationincludes both the STV and the LW of the R-wave amplitude, which is anexample of a morphological feature, but other measures of variationsand/or other morphological features may alternatively or additionally beused. Such other morphological features may include an area under acurve of a QRS complex, R-wave, T-wave, or ST-region; a maximumamplitude of a QRS complex, R-wave, or T-wave; or a peak-to-peak (P2P)amplitude of a QRS complex, R-wave, or T-wave, but are not limitedthereto. Where a patient's heart is paced, the morphological feature(s)can correspond to morphological features of paced cardiac cycles, suchas, but not limited to, the amplitude, peak-to-peak (P2P) amplitude,and/or area under the curve of an evoked response. Both short-term andlong-terms measures of variation (SW and LW) can be determined for oneor more of the above mentioned morphological features, and compared toone another and/or to respective thresholds, as explained in more detailbelow. Instead of (or in addition to) determining at least one measureof variation in one or more morphological features of the signal duringthe measurement period (that includes a plurality of cardiac cycles), atleast one measure of variation can be determined in one or more temporalfeatures of the signal, such as, but not limited to, RR intervaldurations, PR interval durations, QRS durations, and/or QT intervaldurations. Where a patient's heart is paced, the temporal feature(s) cancorrespond to temporal features of paced cardiac cycles, such as, butnot limited to, AR interval durations, PV interval durations, or evokedresponse durations. The measure(s) of variation in the feature of thesignal can be one or more of a standard deviation of the feature, astandard deviation of the feature divided by an average of the feature,a difference between a maximum and a mean of the feature, a differencebetween a maximum and a minimum of the feature, and/or a differencebetween a second maximum and a second mean of the feature (or adifference between then Nth maximum and Nth mean of the feature). Themeasure(s) of variation can alternatively

At step 320, a potential apnea event in the measurement period isdetected based on the at least one measure of variation in the feature.The feature may be a morphological feature or a temporal feature, asnoted above. Additional details of how steps 310 and 320 can beperformed, in accordance with certain embodiments, are described belowin the discussion of FIGS. 4A-4C. However, in general, a potential apneaevent is detected when two measures of variation in one or more featuresof the signal (obtained at step 300) are compared, or at least onemeasure of variation falls below one or more respective specifiedthresholds.

At step 330, if a potential apnea event is detected, a determination ofwhether the potential apnea event is a true apnea event is made byanalyzing a portion of the signal preceding the measurement period. Inother words, at step 330 the potential apnea event can be validated asbeing a true apnea event, or can be rejected as being a false positive.In certain embodiments, step 330 is optional. The detection of apotential apnea event using the present technology (at instances of step320) may result from other changes in cardiac rhythm resulting frommultiple cardiac rhythms such as a pacing rhythm in conjunction with anintrinsic rhythm, one or more premature ventricular contractions (PVCs),or one or more premature atrial contractions (PACs). In other words, atstep 330 there is a determination of whether there was a false positivedetection at step 320, wherein such a false positive detection may haveoccurred due to one or more PVC or PACs, and/or due to a patient's heartactivating both intrinsically and in response to pacing during themeasurement period.

FIG. 4A illustrates one method for performing steps 310 and 320 of FIG.3. At step 400, a SW in the at least one feature is determined. In oneembodiment, feature for which the SW is determined is the peak-to-peakamplitude of an R-wave over three beats, but more or fewer beats may beused (so long as at least two beats are used). At step 410, an LW in theat least one feature (e.g., peak-to-peak R-wave amplitude) is determinedover twenty-five (25) beats, but more or fewer beats may be used. Insteps 400 and 410, one or more measure(s) of variation can be determinedfor alternative and/or additionally morphological feature(s) besides thepeak-to-peak amplitude for R-waves. Steps 400 and 410 show one method ofperforming step 310 of FIG. 3.

Once the STV and the LW are determined at instances of steps 400 and410, a comparison of the STV and LTV is made at step 420, to determinewhether the LW is greater than the STV for at least a specifiedduration. In one embodiment, the specified duration is a minimum of ten(10) seconds. A ten second duration is chosen because a singleapnea/hypopnea event (AHE) is defined as reduced or absent airflow for aduration of 10 seconds. Thus, the method may use a minimum of tenseconds during which SW is lower than LTV to determine whether apotential apnea event has occurred.

At step 430, if the LTV is greater than the STV over the measurementwindow, a potential apnea event is determined to be detected at 440. Ifover a window of at least ten seconds the LTV is greater than the STV,the method moves to determine whether the potential apnea event is anactual apnea event, and ensure that the potential apnea event is notcaused by some other cardiac issue.

In alternative embodiments, the STV alone may be used to determine apotential apnea event. FIG. 4B illustrates one such embodiment. In FIG.4B, like reference numbers indicate like steps to those in FIG. 4A. Step400 is identical to step 400 of FIG. 4A where a SW in the at least onefeature is determined. In one embodiment, feature for which the STV isdetermined is the peak-to-peak amplitude of an R-wave over three beats,but more or fewer beats may be used. At step 460, the SW is compared toa threshold value. At 460, if the SW is below the threshold value forthe measurement period (for example, ten seconds), a potential apneaevent is recognized at step 440. The threshold may be an absolutethreshold or a variable threshold which may be a percentage of the STVfor a period prior to the measurement period, a percentage of the

LW prior to the measurement period, or a percentage of the average SWover some period of the signal prior to the measurement period.

In yet another alternative, both a comparison of the LW to the SW duringthe measurement period and a comparison of the SW to a threshold may beused to determine an apnea event, as illustrated in FIG. 4C. In FIG. 4C,like reference numbers indicate like steps to those in FIGS. 4A and FIG.4B. In FIG. 4C, the comparison at step 430 and the comparison at step460 may be reversed in order.

Although the above description describes intrinsic pacing, thetechnology is operable in patients having active ventricular orV-pacing. In another embodiment, when a patient is known to have apacemaker and is subject to V-pacing, the morphological feature may bemeasured by an area under the curve calculation of the morphologicalfeature.

The above description uses an SW of three beats, but as few as two beatsmay comprise a period for determining an STV. The above description usestwenty-five beats for an LW, but any number greater than the number ofbeats used for the STV may be used for the LTV. In one embodiment,two-times the number of beats used to determine the STV may be used todetermine the LTV (i.e. if three beats is used to determine the STV, anLTV may be determined with six beats).

In one embodiment, the R-wave feature is measured over a time windowmeasuring between 60 msec and 200 msec from an R-wave marker in the ECGsignal, where the peak-to-peak minimum of the R-wave marker issubtracted from the maximum of the marker. When the feature is measuredfrom a V-pacing morphology, the feature may be measured from the averageunder the curve between 0 msec from the initial V-pacing marker to 200msec from the V-pacing marker. The average under the curve is calculatedby summing the absolute values of the feature over the measurementwindow less the value of the feature at the start of the window.

Each of the long and short-term variability can be calculated as thestandard deviation of the feature (R-wave amplitude in this example)divided by the average value of the feature. As noted above, for STV,the calculation can be performed over three beats and for LW thecalculation can be performed over twenty-five beats, in one embodiment.

During an episode of an arrhythmia, temporal features (e.g., RR intervaldurations, PR interval durations, QRS durations, and/or QT intervaldurations) of a signal (e.g., an EGM or ECG signal) indicative ofcardiac electrical activity may be influenced by the arrhythmia, andtemporal features of the EGM/ECG signal that may be useful for detectingan apnea event may be masked by the arrhythmia. Accordingly, in at leastin one embodiment, during an arrhythmic episode, the feature(s) of theECG/EGM for which measures of STV and/or LW are determined and comparedto one another (e.g., at steps 400, 410, and 420) or to a respectivethreshold (e.g., at step 450), for the purpose of detecting a potentialsleep apnea event (e.g., at step 440 or 450), should be one or moremorphological feature(s), rather than one or more temporal feature(s).In other words, in accordance with certain embodiments of the presenttechnology, the usage of temporal feature(s) for the purpose ofdetecting a potential apnea event, are excluded upon and during anarrhythmia detection.

FIG. 5 illustrates one method for performing step 330 of determiningwhether the potential apnea event is a true apnea event or a falsedetection by analyzing a portion of the signal preceding the measurementperiod. In the method of FIG. 5, the STV in the measurement period iscompared against a threshold value. The threshold varies (between one(1) and two (2) in one embodiment) depending on the SW during thescreening window. (As noted above, the STV can be the standard deviationof the feature (R-wave amplitude in this example) divided by the averagevalue of the feature in the screening window.) In one embodiment, ascreening window comprises the twenty-five (25) beats which immediatelyprecede the measurement period. The screening window may be set at thesame number of beats as the LW (25 in the above description) such thatif the LW is measured over more or fewer beats, the screening window isset at the same number of beats. Alternatively, the screening window maybe set at a different number of beats than the number of beats in theLTV calculation.

At step 500, over a screening window comprising a selected number ofbeats (e.g., twenty-five beats) preceding the beginning of themeasurement period, the average of the STV (AVG) during screening windowand a maximum of the STV (MAX) during the measurement period arecalculated.

At step 510, if the average of STV (AVG) during screening window isgreater than or equal to 0.02 seconds then the apnea detection thresholdis set to a first threshold having a numerical value of two (2) at step520. If the average of the SW during the screening window is less than0.02 seconds, then the apnea detection threshold is set to a secondthreshold having a numerical value of one (1) at step 530. Thus, thethreshold for comparison of SW to LTV may vary based on the average andmaximum of the STV during the screening window.

At step 540, a determination is made as to whether the maximum of theSTV during the measurement window (SW (MAX)) divided by the average ofthe STV during the screening window (STV (AVG)) is less than the setthreshold. If not, then the potential apnea event detected (at 440) isdetermined to not be an apnea event at step 580. If so, then the methodmoves to step 550.

Steps 550 and 560 determine whether mixed rhythm types or outliers arepresent in the screening window to ensure that the potential apnea eventdetection was not triggered inappropriately due to morphology changesresulting from changes in cardiac rhythms, PVCs or other conditions,such as noise introduced by electronics or outside influences.

At step 550, a determination is made as to whether more than one type ofcardiac rhythm is present within the screening window. For instance, ifan atrial paced, ventricular paced rhythm is mixed with an atrial paced,ventricular sensed rhythm, or an atrial sensed, ventricular paced rhythmis mixed with an atrial sensed, ventricular sensed rhythm, the potentialapnea event will be determined not to be an apnea event at step 580. Ifonly one type of rhythm is present, then a determination is made at step560 as to whether any peak-to-peak or area under the curve (AUC)outliers exist in the screening window.

In addition, single large amplitude PVCs or areas under the curve withfusion beats can lead to an incorrect apnea event prediction. At step560, the relative difference between a current peak-to-peak or averageunder the curve value of the feature is calculated relative to immediateneighboring values. This may be performed calculating a pre- andpost-difference value of the feature and comparing it to a threshold.The pre-difference value may be calculated by taking the value of a peakless the value of its preceding peak, dividing that sum by the value ofthe preceding peak, and taking the absolute value thereof. Thepost-difference value may be calculated by taking the value of a peakless the value of its succeeding peak, dividing that sum by the value ofthe succeeding peak, and taking the absolute value thereof. If pre- andpost-difference values are greater than or equal to a threshold of, inone embodiment, 0.3, an outlier is determined to be present, and thepotential apnea event is determined to not be an apnea event at step580. For intrinsic R-waves, an additional evaluation at step 560 usingan R-to-R interval criteria is used. If the pre- and post-differencevalues are greater than or equal to a second threshold, for example 0.15in one embodiment, then a comparison of the timing of R-wave detectionsof three different R-waves is made to determine whether an outlier ispresent. This comparison may include comparing whether a differencebetween a first and second successive R-wave detection timing is lessthan eighty (80) percent of the second and a third successive R-wavedetection timing. If so, then an outlier is determined.

At step 570, if apnea events are found in two measurement periodsseparated by less than a number of cardiac beats, for example, fivecardiac beats, it is likely that the two apnea events are a singleevent. Thus, the events may be merged at step 570. At step 590, theapnea event is confirmed.

The Apnea-Hypopnea Index (AHI) is an index used to indicate the severityof sleep apnea. It is represented by the number of apnea and hypopneaevents per hour of sleep. The total number confirmed apnea events may bedivided by the total patient sleep time to provide a daily AHI estimate.

The aforementioned methods of FIGS. 3-5 may be performed in an IMD inreal time as the device conducts other operations within a patient. Inalternative embodiments, an IMD may obtain IEGM signal data and outputthat data to another processing system which performs the analysisdescribed herein. Such other processing systems may be any suitablehardware, software, virtual or cloud-based processing system includingcode operable to instruct the processing system to perform the methodsherein. In an IMD, the MCU may include code operable to instruct the MCUto perform the methods in real time, or upload results of the method forfurther analysis. Such results may include, without limitation, AHI,number and duration of apnea events, and timing of apnea events over asleep period or multiple sleep periods.

Experimental results were obtained by applying the method discussedabove to a data set of patients of who completed a sleep study usingpolysomnography (PSG) and each of whom had an implanted, dual chamberpacemaker. The method reported predictions of clinically moderate/severe(AHI>15) and severe (AHI>30) events. In the results, sensitivity,specificity, positive predictive value (PPV) and negative predictivevalue (NPV) were obtained. The atrial and ventricular EGM from eachpatient's pacemaker was recorded while the patient performed a PSG overthe course of a night of sleep. After acquisition, the PSG and EGM weretime-synchronized for the purpose of analysis. Sensitivities forpredicting AHI of greater than 15 and AHI of greater than 30 were 86%and 88% respectively using the present technology, with positivepredictive values results showing similar results with 86% (12/14) and78% (7/9). Specificity and NPV values are similar as well, asillustrated in table 1.

TABLE 1 Sensitivity (%) Specificity (%) PPV (%) NPV (%) AHI > 15 86 8786 87 AHI > 30 88 90 78 95

Performance of the present technology was also evaluated by ventricularrhythm types (pacing vs. intrinsic). As shown in Table 2, the technologymay perform better in ventricular pacing mode compared to ventricularsensed for both AHI cutoffs of 15 and 30 with a limited number ofpatients (N) for each type.

TABLE 2 Average Sensi- Speci- Perfor- Ventricular tivity ficity PPV NPVmance Rhythm Type (%) (%) (%) (%) (%) AHI > 15 Sensed (N = 13) 88 80 7875 80 Paced (N = 9) 83 100 100 75 90 AHI > 30 Sensed (N = 13) 75 80 7580 78 Paced (N = 9) 100 89 80 100 92

FIG. 6 shows an example of proper detection of one apnea/hypopnea eventby the present technology. FIG. 6 show is an example EGM segment 610 ofapproximately seventy (70) seconds. FIG. 6 also illustrates a plot 620of the P2P amplitude for each cardiac cycle. Block 630 represents ahuman expert-adjudicated apnea/hypopnea event represented by the R-waveamplitude variability of the signal in the segment 630. In block 630,the P2P amplitudes were significantly reduced during the apnea eventreturning to normal amplitudes following the end of the apnea event. Aplot 640 of the STV and a plot 650 of the LW are shown, with block 670representing the detected apnea event by the present technology. Asshown in FIG. 6, the SW crosses LW many times during normal breathingbut became suppressed below the LW in the block 670. The STV thencrosses the LTV, marking the end of the apnea event.

As noted above, in certain embodiments, one of the methods formonitoring for apnea, as described above, is only performed when it isdetermined that a patient is sleeping, or at least likely to besleeping. In other words, sleep apnea is monitored for when the patientis classified as being asleep, and is not monitored for when the patientis classified as being awake. During rest and sleep periods, theactivity levels and rate of change in posture normally decreases in mostpatients. Certain embodiments of the present technology relatetechniques for detecting sleep entry and exit using activity and/orposture data, as obtained from one or more sensors, such as, athree-dimensional (3D) accelerometer.

In accordance with certain embodiments, sleep entry is detected when apatient's activity level drops below a sleep threshold, and thepatient's posture is either recumbent or reclined. In accordance withcertain embodiments, sleep entry is detected when the patient's activitylevel increases above (i.e., exceeds) the sleep threshold, and thepatient's posture is upright, where an upright posture includes anysitting or standing posture. The same component or subsystem that isused for detecting a patient's activity level can also be used to detectthe patient's posture. It would also be possible for a first componentor subsystem to be used for detecting the patient's activity level, anda second component or subsystem to be used for detecting the patient'sposture. However, to minimize the number of components that are locatedwithin an IMD and that consume power from a battery of the IMD, it isbeneficial to utilize the same component to both detect the patient'sactivity level and the patient's posture. Such a component can be amulti-dimensional accelerometer, such as a 3D accelerometer. A patient'sposture is considered to be recumbent if they are lying on their back,stomach, left-side, or right-side. In other words, recumbent postureincludes any of the lying down postures such as left-sided, right-sided,prone, and supine postures. A patient's posture is considered to beupright if the patient is standing or sitting generally upright. Inother words, upright posture includes any of the sitting and standingpostures where the patient's upper body is generally upright, as opposedto reclined or recumbent.

A 3D accelerometer includes sensors that generate first (X), second (Y)and third (Z) accelerometer signals along corresponding X, Y and Z axes(also referred to as first axis accelerometer signals, second axisaccelerometer signals and third axis accelerometer signals). The X, Yand Z axes accelerometer signals collectively define a three-dimensional(3D), or multi-dimensional (MD), accelerometer data set. While examplesherein are described in connection with an accelerometer that generatesaccelerometer signals along three orthogonal axes, it is recognized thatembodiments may be implemented wherein accelerometer signals aregenerated along two or more axes, including more than three axes. A 3Daccelerometer can include one or more analog-to-digital converters(ADCs) that convert analog X, Y, and Z axes accelerometer signals todigital signals, based upon which an activity level and a posture can bedetermined. One example of such a 3D accelerometer is described in U.S.Pat. No. 6,937,900, titled “AC/DC Multi-Axis Accelerometer forDetermining A Patient Activity And Body Position,” the complete subjectmatter which is expressly incorporated herein by reference.

The high level flow diagram of FIG. 7A is used to explain how a 3Daccelerometer can be used to detect sleep entry, in accordance withcertain embodiments of the present technology. Thereafter, the highlevel flow diagram of FIG. 8A is used to explain how the same 3Daccelerometer can be used to detect sleep exit. The term sleep entry, asused herein, refers to a patient's transition from a non-sleep state toa sleep state. The term sleep exit, as used herein, refers to apatient's transition from a sleep state to a non-sleep state. FIG. 9will then be used to explain how an embodiment that is used to monitorfor a potential sleep apnea event can be enabled in response to sleepentry being detected, and can be disabled in response to sleep exitbeing detected, in accordance with specific embodiments of the presenttechnology. Such embodiments are useful for improving the autonomousmonitoring for sleep apnea, because a patient cannot experience sleepapnea when they are awake, i.e., when they are in a non-sleep state.Accordingly, such embodiments can be used to reduce the number ofpotential false positive detections of potential sleep apnea events.

Referring to FIG. 7A, the method summarized therein begins while thepatient is awake (i.e., in a non-sleep state), as indicated by block700. At step 710 there is a determination of the activity level of thepatient, which can be performed by reading the z-axis data from a 3Daccelerometer, but is not limited thereto. At step 720 there is adetermination of whether the activity level is less than a specifiedthreshold, which can be referred to as the sleep threshold. If theanswer to the determination at step 720 is NO, then flow returns to step710. If the answer to the determination at step 720 is YES, then flowgoes to step 730.

At step 730 there is a determination of the patient's posture, which canbe performed by reading data from multiple axis of the 3D accelerometer(e.g., two of the x-, y-, and z-axis), and preferably, three axis of the3D accelerometer (e.g., all three of the x-, y-, and z-axis). At step740 there is a determination of whether the patient's posture isrecumbent or reclined, which are the most likely postures a patient willhave when asleep. If the answer to the determination at step 740 is NO,then flow returns to step 710. If the answer to the determination atstep 740 is YES, then flow goes to step 750.

At step 750 there is a determination of whether a sleep timer (e.g., 134in FIG. 1) has expired. The sleep timer, aka the S_TIMER, can beconfigured to count-up, or count-down, for a specified amount of time(e.g., 10 minutes) once initiated, at which point the sleep timerexpires. The specified amount of time, which can also be referred to asthe sleep latency duration, is how long it is expected for a patient tofall asleep after initially lying down to try to go to sleep. Inaccordance with certain embodiments, the specified amount of time can be10 minutes. However, the use of shorter or longer sleep latencydurations are also possible and within the scope of the embodimentsdisclosed herein. If the answer to the determination at step 750 is NO,the flow goes to step 760.

At step 760 there is a determination of whether the sleep timer has beeninitialized. If the answer to the determination at step 760 is YES, thenflow returns to step 710. If the answer to the determination at step 760is NO, then flow goes to step 770, at which step the sleep timer isinitialized. Initialization of the sleep timer can involve starting thesleep timer, or resetting and then starting the sleep timer. After step770, flow returns to step 710.

Returning to step 750, if the answer to the determination at step 750 isYES, then it is determined that the patient is asleep, as indicated byblock 780. More generally, in the embodiment summarized with referenceto FIG. 7A, a patient will be determined to be asleep (i.e., beclassified as being asleep), in response to the patient both having anactivity level below a specified threshold level and the patient'sposture being either recumbent or reclined, for at least the thresholdamount of time (aka the sleep latency duration).

FIG. 7B shows example pseudocode that can be used to implement themethod summarized with reference to the flow diagram of FIG. 7A. As canbe appreciated from FIG. 7B, a patient can be classified as being asleepif both of the following conditions are satisfied for at least thespecified amount of time (aka the sleep latency duration), e.g., 10minutes: (a) the patient's posture is either recumbent or reclined, and(b) patient's activity level is below the specified activity threshold.The activity threshold can also be referred to herein as the activitylevel threshold.

Reference is now made to the high level flow diagram of FIG. 8A, whichas noted above, is used to explain how the 3D accelerometer can be usedto detect sleep exit, which is a transition from a sleep state to anon-sleep state. Referring to FIG. 8A, at step 810 there is adetermination of the patient's posture, which can be performed byreading data from multiple axis of the 3D accelerometer, and preferably,three axis of the 3D accelerometer. At step 820 there is a determinationof whether the posture is upright, which is the most likely posture apatient will have when awake. If the answer to the determination at step820 is NO, then flow goes to step 830. If the answer to thedetermination at step 820 is YES, then flow goes to step 880, at whichblock it is determined that the patient is awake (i.e., classified asbeing awake). This is because if a patient has transitioned from havinga recumbent or reclined posture, while asleep, to having an uprightposture, the patient has most likely awoken.

If the answer to the determination at step 820 is NO, the flow goes tostep 830. At step 830 there is a determination of the activity level ofthe patient, which can be performed by reading the z-axis data from a 3Daccelerometer, but is not limited thereto. At step 840 there is adetermination of whether the activity level is greater than a specifiedthreshold, which can be referred to as the awake threshold. The awakethreshold used at step 840 can be the same as the sleep threshold usedat 720, however, that need not be the case.

If the answer to the determination at step 840 is NO, then flow returnsto step 810. If the answer to the determination at step 840 is YES, thenflow goes to step 850.

At step 850 there is a determination of whether an awake timer (e.g.,136 in FIG. 1) has expired. The awake timer, aka the W_TIMER, can beconfigured to count-up, or count-down, fora specified amount of time(e.g., 2 minutes) once initiated, at which point the awake timerexpires. The specified amount of time, which can also be referred to asthe awake latency duration, is amount of time after which it is presumeda patient has woken up if the patient's activity level remains above thespecified threshold level for that amount of time. In accordance withcertain embodiments, the specified amount of time can be 2 minutes.However, the use of shorter or longer awake latency durations, and thusshorter or longer awake thresholds, are also possible and within thescope of the embodiments disclosed herein. If the answer to thedetermination at step 850 is NO, the flow goes to step 860.

At step 860 there is a determination of whether the awake timer has beeninitialized. If the answer to the determination at step 860 is YES, thenflow returns to step 810. If the answer to the determination at step 860is NO, then flow goes to step 870, at which step the awake timer isinitialized. Initialization of the awake timer can involve starting theawake timer, or resetting and then starting the awake timer. After step870, flow returns to step 810.

Returning to step 850, if the answer to the determination at step 850 isYES, then it is determined that the patient is awake, as indicated byblock 880. More generally, in the embodiment summarized with referenceto FIG. 8A, a patient will be determined to have be awake, in responseto the patient either having an upright posture, or having an activitylevel above a specified threshold level, for at least the thresholdamount of time (aka the awake latency duration).

FIG. 8B shows example pseudocode that can be used to implement themethod summarized with reference to the flow diagram of FIG. 8A. As canbe appreciated from FIG. 8B, a patient can be classified as being awakeif one of the following conditions are satisfied for at least thespecified amount of time (aka the awake latency duration), e.g., 2minutes: (a) the patient's posture is upright, or (b) patient's activitylevel is above the specified activity threshold.

FIG. 9 will now be used to explain how an embodiment that is used tomonitor for a potential sleep apnea event can be enabled in response tosleep entry being detected, and can be disabled in response to sleepexit being detected, in accordance with specific embodiments of thepresent technology. Such embodiments are useful for improving theautonomous monitoring for sleep apnea, because a patient cannotexperience sleep apnea when they are awake, i.e., when they are in anon-sleep state. Accordingly, such embodiments can be used to reduce thenumber of potential false positive detections of potential sleep apneaevents. The patient is likely to be asleep when they are classified asbeing asleep.

Referring to FIG. 9, at step 902 there is a determination of whether apatient is likely asleep. Such a determination can be made, for example,using the embodiment described above with reference to FIG. 7A. In suchan embodiment, an accelerometer is used to obtain posture informationand activity information, and the detection of when the patient islikely sleeping is based on the posture information and the activityinformation. If the answer to the determination at step 902 is NO, thenflow returns to step 902. If the answer to the determination at step 902is YES, then flow goes to step 904.

At step 904 sleep apnea monitoring is enabled, or if already enabled,continues to be enabled. Such sleep apnea monitoring can includedetermining a measure of STV and a measure of LW of a feature of thesignal indicative of cardiac electrical activity of a patient's heartover a measurement period that includes a plurality of cardiac cycles,wherein the feature comprises one of a morphological feature or atemporal feature, and monitoring for a potential apnea event in themeasurement period based on the measure of SW and the measure of LW inthe feature. In other words, in response to detecting that the patientis likely sleeping, there can be an enabling of the determining of themeasure of STV and the measure of LTV that are used for detecting thepotential apnea event. Additional details of how a measure of STV and ameasure of LW can be used for detecting a potential apnea event aredescribed above with reference to FIGS. 2-6.

Still referring to FIG. 9, at step 906 there is a determination ofwhether a patient is likely awake. Such a determination can be made, forexample, using the embodiment described above with reference to FIG. 8A.In such an embodiment, an accelerometer is used to obtain postureinformation and/or activity information, and the detection of when thepatient is likely awake is based on the posture information, and/orbased on the activity information. If the answer to the determination atstep 906 is NO, then flow returns to step 906. If the answer to thedetermination at step 906 is YES, then flow goes to step 908. At step908 sleep apnea monitoring is disabled, or if already disabled,continues to be disabled. Flow then returns to step 902. The patient islikely to be awake when the patient is classified as being awake.

The amount of sleep patients get plays a vital role in the quality oflife for patients. Precise and accurate methods for detecting sleepentry and exit are important to calculate total sleep period. FIG. 10will now be used to explain how the sleep entry and sleep exittechniques, described above with reference to FIGS. 6 and 7, can be usedto monitor the amount of sleep a patient gets continuously and/or withina 24 hour period, or some other specified period.

Referring to FIG. 10, at step 1002 there is a determination of whether apatient is likely asleep. Such a determination can be made, for example,using the embodiment described above with reference to FIG. 7A. In suchan embodiment, an accelerometer is used to obtain posture informationand activity information, and the detection of when the patient islikely sleeping is based on the posture information and the activityinformation. If the answer to the determination at step 1002 is NO, thenflow returns to step 1002. If the answer to the determination at step1002 is YES, then flow goes to step 1004. At step 1004 a sleep timer(e.g., 134 in FIG. 1, or a separate sleep timer) is started.

Still referring to FIG. 10, at step 1006 there is a determination ofwhether a patient is likely awake. Such a determination can be made, forexample, using the embodiment described above with reference to FIG. 8A.In such an embodiment, an accelerometer is used to obtain postureinformation and/or activity information, and the detection of when thepatient is likely awake is based on the posture information, and/orbased on the activity information. If the answer to the determination atstep 1006 is NO, then flow returns to step 1006. If the answer to thedetermination at step 1006 is YES, then flow goes to step 1008. At step1008 the sleep timer is stopped.

In certain embodiments, each time the sleep timer is stopped, a value ofthe sleep timer is stored in memory (e.g., 120) along with a time stampor the like, which saved value can specified how long a patientcontinuously slept. This assumes the sleep timer is reset each time thepatient transitions from being asleep to being awake, or from beingawake to being asleep. In an alternative embodiment, the sleep timer canbe rest once per day, e.g., at 11:00 a.m. each day, and the sleep timercan be used keep track of a total amount of sleep that a patient getsduring each day (i.e., during each 24 hour period) or during some otherspecified period of time (e.g., a 7 day period, aka a week), and suchinformation can be stored in memory (e.g., 120).

Referring briefly back to the flow diagrams of FIGS. 7A and 8A, at step720 in FIG. 7A and step 840 in FIG. 8A, a patient's activity level iscompared to a threshold, which can be referred to as an activitythreshold. The activity threshold can be a predetermined value, oralternatively, can be a dynamic value that is updated from time to timebased on measurements of activity obtained from an accelerometer (e.g.,125 in FIG. 1). Various ways of producing a dynamic activity threshold,according to various embodiments of the present technology, aredescribed below.

In certain embodiments, described below with reference to FIGS. 11A and11B, a histogram is generated using activity data (e.g., z-axis activitydata) obtained using an accelerometer over a specified period of time(e.g., a 7 day period, aka a week). As an example, activity data can beobtained once per second and used to generate a histogram. An activitythreshold can then be determined as being equal to the activity levelvalue below which there is a specified percent (e.g., 33 percent) of theactivity counts included in the histogram. In alternative embodiments,an activity threshold, for use in the sleep entry and sleep exitdetection techniques described above, is set as being equal to a longterm average (LTA) or a long term moving average (LTMA) of activity data(e.g., z-activity data) obtained using an accelerometer over a specifiedperiod of time (e.g., a 7 day period, aka a week). The activitythreshold can be defined as being equal to the LTA or the LTMA.Alternatively, the activity threshold can be defined as being equal tothe LTA or the LTMA plus a specified offset.

In each of the embodiments described above, which involve comparing adetected activity level to an activity threshold, the activity level andthe activity threshold can be for a single axis of activity data, suchas z-axis activity data. Alternatively, activity level data can beobtained for multiple axis, e.g., two or three axis, and morespecifically two of the x-, y-, and z-axes, or all three of the x-, y-,and z-axes. Where activity level data is obtained and analyzed formultiple axes, the activity level data for the multiple axes can becombined (e.g., added or averaged) and then compared to an appropriateactivity threshold. Alternatively, for each separate axis, obtainedactivity level data can be separately compared to a respective activitythreshold. For example, where activity level data for each of the threeseparate axis is compared to a respective activity threshold (i.e., anx-axis threshold, a y-axis threshold, and a z-axis threshold), thepatient's activity level can be determined to exceed the threshold(s),such that the answer to the determination at step 720 or step 840 isYes, if one of the three thresholds is exceeded, if two of the threethresholds are exceeded, or if all three of the thresholds are exceeded,depending upon the specific implementation. It would also be possible toutilized activity data for just two of the above mentioned three axes,rather than for all three axes. Data from two axes can be combined(e.g., added or averaged) and then compared to an appropriate threshold,or data from each of the two axes can be compared to a respectivethreshold. Where activity level data for each of the two separate axisis compared to a respective activity threshold (i.e., an x-axisthreshold and a z-axis threshold), the patient's activity level can bedetermined to exceed the threshold(s), such that the answer to thedetermination at step 720 or step 840 is Yes, if one of the twothresholds is exceeded, or if both of the two thresholds are exceeded,depending upon the specific implementation.

FIG. 11A is an example graph showing activity data (e.g., z-axisactivity data) collected over a number of days, which includes October22, October 23, October 24, October 25, October 26, and October 27. InFIG. 11A, the days are indicated along the horizontal axis, and activitylevels are indicated along the vertical axis. FIG. 11B illustrates anexample of a histogram produced using the activity data shown in FIG.11A. In FIG. 11B, activity level bins are indicated along the horizontalaxis, and the activity counts within each of the bins is specified alongthe vertical axis. In accordance with an embodiment, an activitythreshold is determined by summing up all of the activity countsincluded in the histogram, and setting the activity threshold as beingequal to a specified percent (e.g., 33 percent) of the total activitycounts. In the example of FIG. 11B, the activity level at 33 percent ofthe total activity counts is 27, meaning in the Example of FIG. 11B theactivity threshold is set to an activity level of 27.

FIG. 12 includes a graph that illustrates how certain embodiments of thepresent technology described above can be used to detect when a patientis asleep over a 24 hour period, between 6 PM of one day and 6 PM of thefollowing day. In FIG. 12, the line labeled 1202 indicates the postureof the patient as determined from accelerometer data or detected by theaccelerometer, the line labeled 1204 indicates the activity level alongthe z-axis as detected by the accelerometer, the dashed line labeled1206 indicates the activity threshold, and the line labeled 1208indicates when the patient is asleep. In this example, the activitythreshold is set to an activity level of 10, the patent is classified asbeing asleep when both of the following conditions are satisfied for atleast the sleep latency duration of 10 minutes: (a) the patient'sposture is either recumbent or reclined, and (b) patient's activitylevel is below the specified activity threshold. Additionally, in thisexample the patient is classified as being awake (aka not asleep) whenone of the following conditions is satisfied for at least the awakelatency duration of 2 minutes: (a) the patient's posture is upright, or(b) patient's activity level is above the specified activity threshold.

As can be appreciated from FIG. 12, the patient was classified as beingasleep from 12:30 AM to 8:30 AM, and thus, was asleep for about 8 hoursconsecutively, and 8 hours total within the 24 hour period. During theother periods of time the patient was classified as being awake. Thephrases classified as being awake and determined to be awake, andsimilar phrases, as used herein, are used interchangeable. Similarly,the phrases classified as being asleep and determined to be asleep, andsimilar phrases, as used herein, are used interchangeably. Where an IMDis configured to only monitor for apnea while a patient is classified asbeing asleep, in the example of FIG. 12, apnea events are only monitoredfor between 12:30 AM and 8:30 AM. Additionally, or alternatively, othermethods (besides apnea detection methods) can be enabled while a patientis classified as being asleep, and can be disabled while a patient isclassified as being awake, or vise-versa. For an example, a method formonitoring of sleep quality may be enabled while a patient is classifiedas being asleep, and may be disabled while a patient is classified asbeing awake. Such a method can, for example, monitor a patient's hearrate variability (HRV) wherein the patient is asleep and can utilize ameasure of HRV to determine the patient's sleep quality, while highermeasures of HRV is indicative of good sleep quality, and lower measuresof HRV is indicative of poor sleep quality. A numeric sleep score ban beproduced, saved, and/or output. Additionally, or alternatively, a sleepquality indicator can be produced, saved, and/or output, examples ofwhich include poor, good, and excellent. Other variations are alsopossible and within the scope of the embodiments described herein.

An algorithm or technique that is used to classify a patient as beingasleep or awake, as disclosed herein, can be used to determine when toenable and disable certain other algorithms or techniques, such as thedetermining of the measure of STV and the measure of LW that are usedfor detecting a potential apnea event, as was described above. Analgorithm or technique that is used to classify a patient as beingasleep or awake, as disclosed herein, can alternatively be usedcompletely independently of the algorithms or techniques describedherein that are use for detecting a potential apnea event, e.g., toselectively enable and disable other algorithms or techniques, and/or tomonitor how long a patient continually sleeps and/or collectively sleepsover a specified period of time. Other variations are also possible andwithin the scope of the embodiments described herein.

It is to be understood that the subject matter described herein is notlimited in its application to the details of construction and thearrangement of components set forth in the description herein orillustrated in the drawings hereof. The subject matter described hereinis capable of other embodiments and of being practiced or of beingcarried out in various ways. Also, it is to be understood that thephraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having” and variations thereof herein ismeant to encompass the items listed thereafter and equivalents thereofas well as additional items. Further, it is noted that the term “basedon” as used herein, unless stated otherwise, should be interpreted asmeaning based at least in part on, meaning there can be one or moreadditional factors upon which a decision or the like is made. Forexample, if a decision is based on the results of a comparison, thatdecision can also be based on one or more other factors in addition tobeing based on results of the comparison.

Embodiments of the present technology have been described above with theaid of functional building blocks illustrating the performance ofspecified functions and relationships thereof. The boundaries of thesefunctional building blocks have often been defined herein for theconvenience of the description. Alternate boundaries can be defined solong as the specified functions and relationships thereof areappropriately performed. Any such alternate boundaries are thus withinthe scope and spirit of the claimed invention. For example, it would bepossible to combine or separate some of the steps shown in FIGS. 3-5 and7A-10.

It is to be understood that the above description is intended to beillustrative, and not restrictive. For example, the above-describedembodiments (and/or aspects thereof) may be used in combination witheach other. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the embodiments ofthe present technology without departing from its scope. While thedimensions, types of materials and coatings described herein areintended to define the parameters of the embodiments of the presenttechnology, they are by no means limiting and are exemplary embodiments.Many other embodiments will be apparent to those of skill in the artupon reviewing the above description. The scope of the embodiments ofthe present technology should, therefore, be determined with referenceto the appended claims, along with the full scope of equivalents towhich such claims are entitled. In the appended claims, the terms“including” and “in which” are used as the plain-English equivalents ofthe respective terms “comprising” and “wherein.” Moreover, in thefollowing claims, the terms “first,” “second,” and “third,” etc. areused merely as labels, and are not intended to impose numericalrequirements on their objects. Further, the limitations of the followingclaims are not written in means—plus-function format and are notintended to be interpreted based on 35 U.S.C. § 112(f), unless and untilsuch claim limitations expressly use the phrase “means for” followed bya statement of function void of further structure.

What is claimed is:
 1. An apparatus, comprising: sensing circuitrycouplable to electrodes and configured to sense a signal indicative ofcardiac electrical activity of a patient's heart; at least one processorconfigured to determine a measure of short-term variation (SW) and ameasure of long-term variation (LW) in a feature of the signal over ameasurement period that includes a plurality of cardiac cycles; anddetect a potential apnea event in the measurement period based on themeasure of STV and the measure of LW in the feature; wherein the featurecomprises one of a morphological feature or a temporal feature.
 2. Theapparatus of claim 1, wherein the apparatus comprises one of animplantable pacemaker, an implantable cardiac defibrillator, animplantable cardiac monitor, or a non-implanted apparatus.
 3. Theapparatus of claim 1, wherein the at least one processor is configuredto detect the potential apnea event by determining that the measure ofLTV is greater than the measure of STV for at least the measurementperiod.
 4. The apparatus of claim 1, wherein the at least one processoris configured to detect the potential apnea event by determining thatthe measure of LTV is greater than the measure of SW for at least themeasurement period, and the measure of SW is less that a specifiedthreshold for at least the measurement period.
 5. The apparatus of claim1, wherein the at least one processor is configured to: identifyindividual cardiac cycles within the signal indicative of cardiacelectrical activity of a patient's heart; determine the measure of STVbased on a first number of the individual cardiac cycles; and determinethe measure of LTV based on a second number of the individual cardiaccycles, wherein the second number is greater than the first number. 6.The apparatus of claim 1, wherein the feature of the signal, for whichthe measure of SW and the measure of LTV are determined over themeasurement period, comprises a morphological feature, which comprisesone of: an area under a curve of a QRS complex, R-wave, T-wave,ST-region, or evoked response; a maximum amplitude of a QRS complex,R-wave, T-wave, or evoked response; or a peak-to-peak amplitude of a QRScomplex, R-wave, T-wave, or evoked response.
 7. The apparatus of claim1, wherein the feature of the signal, for which the measure of SW andthe measure of LTV are determined over the measurement period, comprisesa temporal feature, which comprises one of: RR interval duration; PRinterval duration; QT interval duration; QRS complex duration; ARinterval duration; PV interval duration; or evoked response duration. 8.The apparatus of claim 1, wherein each of the measure of STV and themeasure of LTV in the feature of the signal over the measurement periodis one of: a standard deviation of the feature; a standard deviation ofthe feature divided by an average of the feature; a difference between amaximum and a mean of the feature; a difference between a maximum and aminimum of the feature; a difference between a second maximum and asecond mean of the feature; a difference between then Nth maximum andNth mean of the feature; or any one of said differences divided by anaverage of the feature.
 9. The apparatus of claim 1, further comprisingan accelerometer that alone or in combination with the at least oneprocessor is used to obtain posture information and activityinformation, wherein the at least one processor is further configuredto: classify the patient as being asleep based on the postureinformation and the activity information; classify the patient as beingawake based on at least one of the posture information or the activityinformation; enable the determining of the measure of SW and the measureof LTV that are used to detect the potential apnea event, in response tothe patient being classified as being asleep; and disable thedetermining of the measure of SW and the measure of LTV that are used todetect the potential apnea event, in response to the patient beingclassified as being awake.
 10. The apparatus of claim 1, wherein the atleast one processor is further configured to determine whether thepotential apnea event is a true apnea event or a false detection byanalyzing a portion of the signal preceding the measurement period inwhich the potential apnea event was detected to determine whether aheart rhythm change likely caused the potential apnea event to bedetected.
 11. A method for monitoring for apnea, comprising: obtaining asignal indicative of cardiac electrical activity of a patient's heart;determining a measure of short-term variation (SW) and a measure oflong-term variation (LTV) of a feature of the signal over a measurementperiod that includes a plurality of cardiac cycles; and detecting apotential apnea event in the measurement period based on the measure ofSTV and the measure of LW in the feature; wherein the feature comprisesone of a morphological feature or a temporal feature.
 12. The method ofclaim 11, further comprising: identifying individual cardiac cycleswithin the signal; wherein the measure of SW is determined based on afirst number of the individual cardiac cycles, and the measure of LW isdetermined based on a second number of the individual cardiac cycles,wherein the second number is greater than the first number.
 13. Themethod of claim 11, further comprising: determining whether thepotential apnea event that is detected is a true apnea event or a falsedetection by analyzing a portion of the signal preceding the measurementperiod in which the potential apnea event was detected to determinewhether a heart rhythm change, a presence of a non-cardiac signal, or achange in the feature is due to a change in patient posture or patientactivity likely caused the potential apnea event to be detected; and inresponse to determining that the potential apnea event is a true apneaevent, storing or uploading information about the true apnea event sothat the information can be accessed by a medical practitioner; whereinthe heart rhythm change comprises at least one of: multiple cardiacrhythms comprising a pacing rhythm in conjunction with an intrinsicrhythm; one or more premature ventricular contractions; or one or morepremature atrial contractions.
 14. The method of claim 11, wherein thedetecting the potential apnea event occurs in response to the measure ofLW of the feature being greater than the measure of STV of the featurefor at least the measurement period.
 15. The method of claim 11, whereinthe detecting the potential apnea event occurs in response to both themeasure of LW of the feature being greater than the measure of STV forat least the measurement period, and the measure of STV of the featurebeing less that a specified threshold for at least the measurementperiod.
 16. The method of claim 11, wherein the feature of the signal,for which the measure of SW and the measure of LTV are determined overthe measurement period, comprises a morphological feature, whichcomprises one of: an area under a curve of a QRS complex, R-wave,T-wave, ST-region, or evoked response; a maximum amplitude of a QRScomplex, R-wave, T-wave, or evoked response; or a peak-to-peak amplitudeof a QRS complex, R-wave, T-wave, or evoked response.
 17. The method ofclaim 11, wherein the feature of the signal, for which the measure of SWand the measure of LTV are determined over the measurement period,comprises a temporal feature, which comprises one of: RR intervalduration; PR interval duration; QT interval duration; QRS complexduration; AR interval duration; PV interval duration; or evoked responseduration.
 18. The method of claim 11, wherein each of the measure of SWand the measure of LW in the feature of the signal over the measurementperiod is one of: a standard deviation of the feature; a standarddeviation of the feature divided by an average of the feature; adifference between a maximum and a mean of the feature; a differencebetween a maximum and a minimum of the feature; a difference between asecond maximum and a second mean of the feature; a difference betweenthen Nth maximum and Nth mean of the feature; or any one of saiddifferences divided by an average of the feature.
 19. The method ofclaim 11, further comprising: using an accelerometer to obtain postureinformation and activity information; during a first period of time,classifying the patient as being asleep based on the posture informationand the activity information; in response to classifying the patient asbeing asleep, enabling the determining of the measure of STV and themeasure of LTV that are used for detecting the potential apnea event;during at second period of time, classifying the patient as being awakebased on at least one of the posture information or the activityinformation; and in response to classifying the patient as being awake,disabling the determining of the measure of STV and the measure of LTVthat are used for detecting the potential apnea event.
 20. Anon-transitory computer readable medium storing instructions to detectan apnea event that, when executed by a processor, causes the processorto perform operations, comprising: obtaining a signal indicative ofcardiac electrical activity of a patient's heart; determining a measureof short-term variation (SW) and a measure of long-term variation (LTV)in a feature of the signal over a measurement period that includes aplurality of cardiac cycles; and detecting a potential apnea event inthe measurement period based on the measure of STV and the measure of LWin the feature; wherein the feature comprises one of a morphologicalfeature or a temporal feature.